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All Journal Data Science: Journal of Computing and Applied Informatics
Surbakti, Suzi
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Computer Science & IT

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Prediction of Dengue Fever in Coastal Areas of North Sumatera (Kuala Namu and Belawan) With Random Forest and Support Vector Machine (SVM) Methods Surbakti, Suzi; Hayatunnufus, Hayatunnufus; T. Henny Febriana
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i2-14355

Abstract

Dengue Fever is a really infectious disease. This disease may cause death. The lack of health facilities in several regions can increase the number of cases and death. Thus, a proper prevention is needed so the number of cases can be decreased and the spread of the fever can be prevented especially in remote area like the coast area of North Sumatera. Because of this, a system that can predict the number of cases based on several parameters is needed to prevent the spread of fever in several areas, using Random Forest dan Support Vector Machine method. Both methods have different forecast results but the number is close to the actual number of cases. Random Forest can predict more accurate with MSE value at 43.
Co-Authors Hayatunnufus Hayatunnufus T. Henny Febriana